Decision-Making with Cause-and-Effect Analysis and DOE

Business process improvements, the grail of any company’s operations, translate directly into better profits by cutting costs and increasing competitiveness at the same time. In many cases, business process improvements have accelerating cumulative effects on company profits. If an insurance company, for example, can underwrite policies faster or settle claims faster, it is providing better service and can do it at a lower cost; thus competing better with nimbler, smaller online competitors.

Unfortunately, any company has a limited amount of money to spend on business process improvement, and that budget has to compete with many other priorities. If those in charge of operations had the choice, they would put in new hardware and software, hire better qualified people, provide them more training, and have a better work environment. However, practical considerations force every company to pick and choose how much to spend on which priorities. The question is – what are the right priorities? How does a company know that the training course on people skills will actually make a difference in customer satisfaction? How does the company know that the expensive CRM (customer relationship management) system it is considering is going to make any difference? How does it know which one to do first?

This is where cause-and-effect analysis, combined with careful design of experiments (DOE), can provide a Six Sigma company with the data to make the most cost-effective decisions. Many companies perform design of experiments without realizing it. For example, before deciding whether to acquire a new CRM software application, a company may have a small group of customer service agents try it out first. Or a company may decide to send a small group of agents to a new training course to test whether the training makes any difference in the quality of service they provide. A combination of cause-and-effect analysis and DOE is a formal and more scientific approach to doing the same things a company may have been doing informally.

Cause-and-effect analysis is a systematic way of generating and sorting hypotheses about possible causes of a problem. Once the root causes of problems are identified, they can be addressed rather than just the symptoms.

A DOE is a structured, organized method for determining the relationship between factors affecting a process and the output of that process. The output of the process is the dependent variable that depends upon the independent variables to determine its outcome.

Cause-and-effect Analysis

A customer service business process is a good example. The company is trying to track down the causes for poor customer service and fix them. A simple cause-and-effect analysis could look something like Figure 1.

Figure 1: Cause-and Effect Analysis (3M&P) for Customer Service

Here, the root causes that determine how good or how bad the end product of customer service might be are hypothesized and sorted into a standard 3M&P model:

Methods: Methods are the processes and procedures used by customer service to deliver its services. These could be:

Call workflow – Poor customer service, real or perceived by the customer, could be an artifact of how call workflow is implemented within the organization. How annoying is it to wait on hold or be passed from person to person when calling for customer service?

Call assignment – How are calls assigned? Does the customer reach the right person who can solve the problem, the very first time?

Call escalation – If the first customer service person cannot solve the problem, who does the customer talk to next? Does that help?

Materials: In the context of customer service, these are the policies, work environment, incentive and reward structures set up within the company for the customer service agents:

Work environment – Customer service is bound to be poor if the work environment of the person delivering it is poor.

Incentive structure – Metrics drive behavior. If customer service agents are measured on how fast they close calls alone (average handling time), their incentive is to close calls whether or not they have solved the customer’s problem.

Machine: In the context of customer service, these are the tools available to the agents to do their jobs:

CRM application – These days, most customer service agents use a customer relationship management system to keep track of all customer interactions. How good customer service is depends upon how well the CRM system is set up and fulfills the precise needs of the agents when providing service.

Problem knowledge base – Many organizations use a problem knowledge base to see if the same problem has been solved for another customer.

People: For customer service to be good, the agents must have certain skills:

Domain skills – A customer service person trying to resolve a computer hardware problem needs to have the particular domain knowledge to be of help.

Problem-solving skills – Customer service delivered over the phone requires a rather systematic approach to problem-solving – eliminating obvious causes for a problem in narrowing it down to the root causes.

People skills – Perception of the quality of customer service depends to a large extent upon the people skills of the agent.

Under the 3M&P cause-and-effect analysis, the quality of customer service can depend upon many factors. How does this relate to design of experiments? Good customer service depends to a large extent on the above factors, but how does a company decide that spending money on training is a more prudent investment than investing in a new CRM system? This is where DOE provides a way of measuring the relative efficacies of one cause over another.

Design of Experiments

The Pareto principle (the 80/20 rule) applies to customer service as much as to any other process within a company. Thus 80 percent of the improvement in customer service is likely to come from 20 percent of the causes above. The question is, which 20 percent?

To address this question with an example DOE exercise, consider the quality of customer service provided as the dependent variable and the factors identified in the cause-and-effect analysis as the independent variables. The experiments which could be done include the following:

Selective training of a sub-group of agents – A sub-group of agents become the experimental group while the rest of the agents become the control group. Now if the company can measure the quality of customer service in some objective way (say, a comprehensive customer satisfaction survey), the company could compare the results of the experimental group with that of the control group to see the extent to which a training course improves agent performance.

Sub-group of agents using a new CRM system – If the company is implementing a pilot project of a new CRM system, have a sub-group of agents use it initially. Their quality of service compared with the rest of the agents will give the company some idea of the real effects of the CRM system on agent performance.

Trying out a proposed incentive structure on a sub-group of agents – Before rolling out a new incentive structure to all agents, try it out on a sub-group of agents and see how it affects agent performance.

Trying out a new workflow or escalation process – New workflow processes or escalation processes are constantly experimented with in companies. Performing a process as a DOE exercise helps the company measure the results in as scientific a way as possible.

Setting up of a proof of concept of a new knowledge base system – Try out a new knowledge base system on a few agents first and measure their performance as a DOE exercise.

This is a gross simplification of the kinds of information that DOE can provide. The above correlations of single factors as a determinant of quality of customer service can be analyzed using analysis of variance (ANOVA) to see how related quality of customer service is to any of the above factors. One-way ANOVA relates one of the independent variables to the dependent variable – in this case, quality of customer service. Sometimes in practice, the combination of two factors is really worth more than just the two factors added up together. For example, experienced customer service managers know that good problem-solving skills, combined with a powerful knowledge base, can improve the quality of service dramatically. Two-way ANOVA can help consider two factors together and analyze their effects on the quality of service on the whole, with proof obtained from data collected when processes are executed.

The key in the above analyses is collection of data. When collecting process execution data, it is just a simple additional step to collect data that has details such as the agent, years of experience, skill levels, which CRM system was used (older or the newer one being considered), etc. When process execution data is collected this way, doing DOE becomes just extracting subsets of data already in hand and analyzing them.

Conclusion: A Powerful Combination of Tools

Cause-and-effect analysis together with DOE is a very powerful combination for business process improvement. In practice, many factors seem to be qualitatively related to the business process performance to be improved. However, when it comes to measuring the precise correlation between the process performance and any one factor, cause-and-effect analysis is needed to catalog the independent variables that are being tweaked. And DOE is needed as a guide in identifying which factors are worth more than others.